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architectures for both GenAI (LLMs, diffusion models) and traditional ML models Build and maintain real-time and batch inference pipelines with high availability and fault tolerance Optimize AI workloads
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teams to enable model training and inference at scale 4) Data Integration, Quality, and Governance Lead ingestion architecture, streaming frameworks, and API integrations. Implement data quality, lineage
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. Experience with Snowflake’s AI/ML stack (Cortex, Snowpark ML, Vector Stores). Experience with DBT Cloud, GitHub Actions, CI/CD for data pipelines. Experience enabling AI workflows in Snowflake (RAG, inference
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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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approaches to research questions; deepen their understanding of causal inference; and recognize the provisional nature of scientific knowledge. Covers issues of statistical methods and data analysis; however
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to academic leadership. Renowned for his work in Big Data and healthcare innovation, Dr. Madigan has authored over 200 publications covering topics such as Bayesian statistics, text mining, and probabilistic
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to academic leadership. Renowned for his work in Big Data and healthcare innovation, Dr. Madigan has authored over 200 publications covering topics such as Bayesian statistics, text mining, and probabilistic
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) and traditional ML models Build and maintain real-time and batch inference pipelines with high availability and fault tolerance Optimize AI workloads for performance, cost-efficiency, and low-latency
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determinants of health with a focus on cognitive decline/dementia and an emphasis on the application of epidemiologic, econometric, and other methods to strengthen causal inference using multilevel, longitudinal
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students to deploy AI systems in up to 200 homes in Atlanta, GA. You will be responsible for designing and deploying the infrastructure that connects sensors, AI inference systems, large foundational models